losses.py 文件源码

def sparse_cross_entropy_loss(logits, labels,
                       weight=1.0, scope=None):
  """Define a Cross Entropy loss using sparse_softmax_cross_entropy_with_logits.

  It can scale the loss by weight factor, and smooth the labels.

  Args:
    logits: [batch_size, num_classes] logits outputs of the network .
    labels: [batch_size,] target labels.
    weight: scale the loss by this factor.
    scope: Optional scope for op_scope.

  Returns:
    A tensor with the softmax_cross_entropy loss.
  """
  with tf.op_scope([logits, labels], scope, 'SparseCrossEntropyLoss'):
    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits,labels,name='xentropy')
    weight = tf.convert_to_tensor(weight,
                                    dtype=logits.dtype.base_dtype,
                                    name='loss_weight')

    loss = tf.mul(weight, tf.reduce_mean(cross_entropy), name='value')
    tf.add_to_collection(LOSSES_COLLECTION, loss)
    return loss